Intelligent Brain Tumor Detector
Author | Abdelhamid, Mostafa |
Author | Alhato, Mohammed |
Author | Elmancy, Ali |
Author | Al-Maadeed, Somaya |
Author | El Harrouss, Omar |
Available date | 2024-10-14T08:51:47Z |
Publication Date | 2023-01-01 |
Publication Name | 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 |
Identifier | http://dx.doi.org/10.1109/ISNCC58260.2023.10323954 |
Citation | Abdelhamid, M., Alhato, M., Elmancy, A., Al-Máadeed, S., & El Harrouss, O. (2023, October). Intelligent Brain Tumor Detector. In 2023 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE. |
ISBN | [9798350335590] |
Abstract | A major challenge in brain tumor treatment planning is determination of the tumor extent. Brain tumor disease can be identified with imaging techniques such as MRI. The images produced by an MRI scan can provide a clear view of the brain's internal structures, including the presence of any abnormal growths or tumors. Tumors can be seen on the images as areas of abnormal tissue that have a different signal intensity from normal brain tissue. In addition, the MRI images can also provide information about the size, shape, location, and characteristics of the tumor, such as its blood flow and whether it is solid or cystic. This information can be very helpful in determining the best course of treatment for the patient. The main objective of this paper is to recognize the existence of tumors in the brain from MRI images using machine learning techniques. Our results show that the k-nearest approach is capable of precisely identifying brain cancers with more than 97%. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | brain image patient treatment tumor |
Type | Conference Paper |
Pagination | 1-6 |
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